Forest canopy closure estimation in mountainous southwest China using multi-source remote sensing data by Wenwu Zhou, Wenwu Zhou, Qingtai Shu, Cuifen Xia, Li Xu, Qin Xiang, Lianjin Fu, Zhengdao Yang, Shuwei Wang

“…Then, the multi-source remote sensing image Sentinel-1/2 and terrain factors were combined to perform regional-scale FCC remote sensing estimation based on the geographically weighted regression (GWR) model. The research results showed that (1) among the 50 extracted ATLAS LiDAR feature indices, the best footprint-scale modeling factors are Landsat_perc, h_dif_canopy, asr, h_min_canopy, toc_roughness, and n_touc_photons after random forest (RF) feature variable optimization; (2) among the BO-RFR, BO-KNN, and BO-GBRT models developed at the footprint scale, the FCC results estimated by the BO-GBRT model were the best (R2 = 0.65, RMSE = 0.10, RS = 0.079, and P = 79.2%), which was used as the FCC estimation model for 74,808 footprints in the study area; (3) taking the FCC value of ATLAS footprint scale in forest land as the training sample data of the regional-scale GWR model, the model accuracy was R2 = 0.70, RMSE = 0.06, and P = 88.27%; and (4) the R² between the FCC estimates from regional-scale remote sensing and the measured values is 0.70, with a correlation coefficient of 0.784, indicating strong agreement. …”
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Machine-Learning-Based Integrated Mining Big Data and Multi-Dimensional Ore-Forming Prediction: A Case Study of Yanshan Iron Mine, Hebei, China by Yuhao Chen, Gongwen Wang, Nini Mou, Leilei Huang, Rong Mei, Mingyuan Zhang

“…Combined with spectral and elemental analysis, the universality of alteration features such as chloritization and carbonation, which are closely related to the mineralization process, was further verified. (3) Based on the spectral and elemental grade data of rock and mineral samples, a training model for ore grade–spectrum correlation was constructed using Random Forests, Support Vector Machines, and other algorithms, with the SMOTE algorithm applied to balance positive and negative samples. …”
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Combination of Conditioning Factors for Generation of Landslide Susceptibility Maps by Extreme Gradient Boosting in Cuenca, Ecuador by Esteban Bravo-López, Tomás Fernández, Chester Sellers, Jorge Delgado-García

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Decomposition pipeline for large-scale portfolio optimization with applications to near-term quantum computing by Atithi Acharya, Romina Yalovetzky, Pierre Minssen, Shouvanik Chakrabarti, Ruslan Shaydulin, Rudy Raymond, Yue Sun, Dylan Herman, Ruben S. Andrist, Grant Salton, Martin J. A. Schuetz, Helmut G. Katzgraber, Marco Pistoia

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Evaluating regional sustainable energy potential through hierarchical clustering and machine learning by Selen Avcı Azkeskin, Zerrin Aladağ

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House Price Prediction of Real Time Data (DHA Defence) Karachi Using Machine Learning by Lata Bai Gokalani, Bhagwan Das, Dilip Kumar Ramnani, Mahender Kumar, Mazhar Ali Shah

“…It is one of the main contribution of the work is that through this the house prediction model based on DHA Karachi data is developed and as per best of our knowledge till today there is no prediction of housing for the country’s important has been developed. has This research paper mainly focuses on real time Defense Housing Authority (DHA) Karachi data, applying different regression algorithms like Decision tree, Random forest and linear regression to find the sales price prediction of the house and compare the performance of these models. …”
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Comparison of machine learning models for coronavirus prediction by B. K. Amos, I. V. Smirnov, M. M. Hermann

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Prognostic Risk Signature and Comprehensive Analyses of Endoplasmic Reticulum Stress-Related Genes in Lung Adenocarcinoma by CaiZhen Yang, YuHui Wei, WenTao Li, JinMei Wei, GuoXing Chen, MingPeng Xu, GuangNan Liu

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Bile acids in follicular fluid: potential new therapeutic targets and predictive markers for women with diminished ovarian reserve by Shu Ding, Wenyan Li, Xianglei Xiong, Manfei Si, Chuyu Yun, Yuqian Wang, Lixuan Huang, Sen Yan, Xiumei Zhen, Jie Qiao, Xinyu Qi

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Groundwater Level Forecasting Using Machine Learning: A Case Study of the Baekje Weir in Four Major Rivers Project, South Korea by Sooyeon Yi, G. Mathias Kondolf, Samuel Sandoval Solis, Larry Dale

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Comparison of Satellite-based PM2.5 Estimation from Aerosol Optical Depth and Top-of-atmosphere Reflectance by Heming Bai, Zhi Zheng, Yuanpeng Zhang, He Huang, Li Wang

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Joint modelling of longitudinal data: a scoping review of methodology and applications for non-time to event data by Rehema K. Ouko, Mavuto Mukaka, Eric O. Ohuma

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4SQR-Code: A 4-state QR code generation model for increasing data storing capacity in the Digital Twin framework by Ababil Islam Udoy, Muhammad Aminur Rahaman, Md. Jahidul Islam, Anichur Rahman, Zulfiqar Ali, Ghulam Muhammad

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A Comparative Study of Loan Approval Prediction Using Machine Learning Methods by Vahid Sinap

“…Machine learning models can automate this process and make the lending process faster and more efficient. In this context, the main objective of this research is to develop models for loan approval prediction using machine learning algorithms such as Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, and Random Forest and to compare their performances. …”
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Machine Learning Model for Detecting Attack in Service Supply Chain by ASMAU OYINLADE OLANIYI, O. A Ayeni, M. G. Adewunmi

“…The study employs machine learning methods to increase the detection of service supply chain attacks, including Decision Trees, Random Forest, and XGBoost algorithms. These models were assessed in accordance with accuracy, precision, recall, and the F1-score, with Random Forest topping the list with an accuracy of 96.1%, followed by Decision Trees with 95.0% accuracy and XGBoost with 94.7% accuracy. …”
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Prediction of prostate biopsy outcomes at different cut-offs of prostate-specific antigen using machine learning: a multicenter study by Mostafa A. Arafa, Karim H. Farhat, Sherin F. Aly, Farrukh K. Khan, Alaa Mokhtar, Abdulaziz M. Althunayan, Waleed Al-Taweel, Sultan S. Al-Khateeb, Sami Azhari, Danny M. Rabah

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AI in Medical Questionnaires: Innovations, Diagnosis, and Implications by Xuexing Luo, Yiyuan Li, Jing Xu, Zhong Zheng, Fangtian Ying, Guanghui Huang

“… This systematic review aimed to explore the current applications, potential benefits, and issues of artificial intelligence (AI) in medical questionnaires, focusing on its role in 3 main functions: assessment, development, and prediction. …”
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Improved mapping of highland bamboo forests using Sentinel-2 time series and machine learning in Google Earth Engine by Dagnew Yebeyen, Binyam Tesfaw Hailu, Worku Zewdie, Temesgen Abera, Gudeta W. Sileshi, Melaku Getachew, Sileshi Nemomissa

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Integrated Single-Cell and Spatial Transcriptomic Analysis Identifies ISR-Related Genes Driving Immune Regulation in Parkinson’s Disease by Jiang H, Zhang X, Feng S, Feng W

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Causal Unit Selection using Tractable Arithmetic Circuits by Haiying Huang, Adnan Darwiche

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